Overview

Dataset statistics

Number of variables28
Number of observations951
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory247.8 KiB
Average record size in memory266.8 B

Variable types

Categorical9
Numeric19

Alerts

pro_authoritarianism_score is highly overall correlated with pro_taxes_score and 12 other fieldsHigh correlation
pro_taxes_score is highly overall correlated with pro_authoritarianism_score and 10 other fieldsHigh correlation
pro_gunrights_score is highly overall correlated with pro_taxes_score and 2 other fieldsHigh correlation
pro_healthcare_score is highly overall correlated with pro_authoritarianism_score and 12 other fieldsHigh correlation
pro_immigrants_score is highly overall correlated with pro_authoritarianism_score and 10 other fieldsHigh correlation
pro_supporting_the_poor_score is highly overall correlated with pro_authoritarianism_score and 10 other fieldsHigh correlation
environmentalist_score is highly overall correlated with pro_authoritarianism_score and 12 other fieldsHigh correlation
economic_populist_score is highly overall correlated with pro_authoritarianism_score and 11 other fieldsHigh correlation
pro_military_score is highly overall correlated with pro_authoritarianism_score and 12 other fieldsHigh correlation
pro_regulation_score is highly overall correlated with pro_gunrights_score and 3 other fieldsHigh correlation
traditionalist_score is highly overall correlated with pro_authoritarianism_score and 11 other fieldsHigh correlation
compassionate_score is highly overall correlated with pro_authoritarianism_score and 12 other fieldsHigh correlation
pro_free_trade_score is highly overall correlated with pro_taxes_score and 4 other fieldsHigh correlation
pro_globalism_score is highly overall correlated with pro_healthcare_score and 7 other fieldsHigh correlation
pro_healthcare_women_score is highly overall correlated with pro_authoritarianism_score and 12 other fieldsHigh correlation
pro_populism_score is highly overall correlated with pro_taxes_score and 4 other fieldsHigh correlation
presidential_election_turnout_score is highly overall correlated with pro_authoritarianism_score and 11 other fieldsHigh correlation
racial_resentment_score is highly overall correlated with pro_authoritarianism_score and 12 other fieldsHigh correlation
pro_religious_freedom_score is highly overall correlated with pro_authoritarianism_score and 10 other fieldsHigh correlation
region is highly overall correlated with countyHigh correlation
county is highly overall correlated with regionHigh correlation
trust_in_institutions_score is highly imbalanced (96.7%)Imbalance

Reproduction

Analysis started2023-02-01 05:33:51.959390
Analysis finished2023-02-01 05:34:32.811162
Duration40.85 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
yes
616 
no
283 
dont_know
 
52

Length

Max length9
Median length3
Mean length3.0304942
Min length2

Characters and Unicode

Total characters2882
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowyes
3rd rowno
4th rowno
5th rowyes

Common Values

ValueCountFrequency (%)
yes 616
64.8%
no 283
29.8%
dont_know 52
 
5.5%

Length

2023-01-31T23:34:32.875218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T23:34:32.961841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
yes 616
64.8%
no 283
29.8%
dont_know 52
 
5.5%

Most occurring characters

ValueCountFrequency (%)
y 616
21.4%
e 616
21.4%
s 616
21.4%
n 387
13.4%
o 387
13.4%
d 52
 
1.8%
t 52
 
1.8%
_ 52
 
1.8%
k 52
 
1.8%
w 52
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2830
98.2%
Connector Punctuation 52
 
1.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
y 616
21.8%
e 616
21.8%
s 616
21.8%
n 387
13.7%
o 387
13.7%
d 52
 
1.8%
t 52
 
1.8%
k 52
 
1.8%
w 52
 
1.8%
Connector Punctuation
ValueCountFrequency (%)
_ 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2830
98.2%
Common 52
 
1.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
y 616
21.8%
e 616
21.8%
s 616
21.8%
n 387
13.7%
o 387
13.7%
d 52
 
1.8%
t 52
 
1.8%
k 52
 
1.8%
w 52
 
1.8%
Common
ValueCountFrequency (%)
_ 52
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2882
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
y 616
21.4%
e 616
21.4%
s 616
21.4%
n 387
13.4%
o 387
13.4%
d 52
 
1.8%
t 52
 
1.8%
_ 52
 
1.8%
k 52
 
1.8%
w 52
 
1.8%

region
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
east
390 
west
191 
south
186 
north
184 

Length

Max length5
Median length4
Mean length4.3890641
Min length4

Characters and Unicode

Total characters4174
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwest
2nd roweast
3rd roweast
4th roweast
5th rowwest

Common Values

ValueCountFrequency (%)
east 390
41.0%
west 191
20.1%
south 186
19.6%
north 184
19.3%

Length

2023-01-31T23:34:33.033924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T23:34:33.115999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
east 390
41.0%
west 191
20.1%
south 186
19.6%
north 184
19.3%

Most occurring characters

ValueCountFrequency (%)
t 951
22.8%
s 767
18.4%
e 581
13.9%
a 390
9.3%
o 370
 
8.9%
h 370
 
8.9%
w 191
 
4.6%
u 186
 
4.5%
n 184
 
4.4%
r 184
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4174
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 951
22.8%
s 767
18.4%
e 581
13.9%
a 390
9.3%
o 370
 
8.9%
h 370
 
8.9%
w 191
 
4.6%
u 186
 
4.5%
n 184
 
4.4%
r 184
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 4174
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 951
22.8%
s 767
18.4%
e 581
13.9%
a 390
9.3%
o 370
 
8.9%
h 370
 
8.9%
w 191
 
4.6%
u 186
 
4.5%
n 184
 
4.4%
r 184
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 951
22.8%
s 767
18.4%
e 581
13.9%
a 390
9.3%
o 370
 
8.9%
h 370
 
8.9%
w 191
 
4.6%
u 186
 
4.5%
n 184
 
4.4%
r 184
 
4.4%

county
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
cuya
604 
angelo
184 
duchess
76 
llandilo
65 
tamale
 
22

Length

Max length8
Median length4
Mean length4.9463722
Min length4

Characters and Unicode

Total characters4704
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowduchess
2nd rowllandilo
3rd rowcuya
4th rowcuya
5th rowcuya

Common Values

ValueCountFrequency (%)
cuya 604
63.5%
angelo 184
 
19.3%
duchess 76
 
8.0%
llandilo 65
 
6.8%
tamale 22
 
2.3%

Length

2023-01-31T23:34:33.200075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T23:34:33.288155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
cuya 604
63.5%
angelo 184
 
19.3%
duchess 76
 
8.0%
llandilo 65
 
6.8%
tamale 22
 
2.3%

Most occurring characters

ValueCountFrequency (%)
a 897
19.1%
c 680
14.5%
u 680
14.5%
y 604
12.8%
l 401
8.5%
e 282
 
6.0%
n 249
 
5.3%
o 249
 
5.3%
g 184
 
3.9%
s 152
 
3.2%
Other values (5) 326
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4704
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 897
19.1%
c 680
14.5%
u 680
14.5%
y 604
12.8%
l 401
8.5%
e 282
 
6.0%
n 249
 
5.3%
o 249
 
5.3%
g 184
 
3.9%
s 152
 
3.2%
Other values (5) 326
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 4704
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 897
19.1%
c 680
14.5%
u 680
14.5%
y 604
12.8%
l 401
8.5%
e 282
 
6.0%
n 249
 
5.3%
o 249
 
5.3%
g 184
 
3.9%
s 152
 
3.2%
Other values (5) 326
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 897
19.1%
c 680
14.5%
u 680
14.5%
y 604
12.8%
l 401
8.5%
e 282
 
6.0%
n 249
 
5.3%
o 249
 
5.3%
g 184
 
3.9%
s 152
 
3.2%
Other values (5) 326
 
6.9%

education
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
college_graduate
304 
some_college
291 
post_graduate
223 
high_school
133 

Length

Max length16
Median length13
Mean length13.373291
Min length11

Characters and Unicode

Total characters12718
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcollege_graduate
2nd rowcollege_graduate
3rd rowcollege_graduate
4th rowcollege_graduate
5th rowsome_college

Common Values

ValueCountFrequency (%)
college_graduate 304
32.0%
some_college 291
30.6%
post_graduate 223
23.4%
high_school 133
14.0%

Length

2023-01-31T23:34:33.363223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T23:34:33.444297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
college_graduate 304
32.0%
some_college 291
30.6%
post_graduate 223
23.4%
high_school 133
14.0%

Most occurring characters

ValueCountFrequency (%)
e 2008
15.8%
o 1375
10.8%
l 1323
10.4%
g 1255
9.9%
a 1054
8.3%
_ 951
7.5%
t 750
 
5.9%
c 728
 
5.7%
s 647
 
5.1%
r 527
 
4.1%
Other values (6) 2100
16.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11767
92.5%
Connector Punctuation 951
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2008
17.1%
o 1375
11.7%
l 1323
11.2%
g 1255
10.7%
a 1054
9.0%
t 750
 
6.4%
c 728
 
6.2%
s 647
 
5.5%
r 527
 
4.5%
d 527
 
4.5%
Other values (5) 1573
13.4%
Connector Punctuation
ValueCountFrequency (%)
_ 951
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11767
92.5%
Common 951
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2008
17.1%
o 1375
11.7%
l 1323
11.2%
g 1255
10.7%
a 1054
9.0%
t 750
 
6.4%
c 728
 
6.2%
s 647
 
5.5%
r 527
 
4.5%
d 527
 
4.5%
Other values (5) 1573
13.4%
Common
ValueCountFrequency (%)
_ 951
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12718
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2008
15.8%
o 1375
10.8%
l 1323
10.4%
g 1255
9.9%
a 1054
8.3%
_ 951
7.5%
t 750
 
5.9%
c 728
 
5.7%
s 647
 
5.1%
r 527
 
4.1%
Other values (6) 2100
16.5%

ses
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
mid_ses
297 
wealthy
260 
dont_know
167 
very_wealthy
140 
low_ses
87 

Length

Max length12
Median length7
Mean length8.0872766
Min length7

Characters and Unicode

Total characters7691
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmid_ses
2nd rowwealthy
3rd rowwealthy
4th rowvery_wealthy
5th rowwealthy

Common Values

ValueCountFrequency (%)
mid_ses 297
31.2%
wealthy 260
27.3%
dont_know 167
17.6%
very_wealthy 140
14.7%
low_ses 87
 
9.1%

Length

2023-01-31T23:34:33.528421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T23:34:33.619087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
mid_ses 297
31.2%
wealthy 260
27.3%
dont_know 167
17.6%
very_wealthy 140
14.7%
low_ses 87
 
9.1%

Most occurring characters

ValueCountFrequency (%)
e 924
12.0%
s 768
10.0%
_ 691
 
9.0%
w 654
 
8.5%
t 567
 
7.4%
y 540
 
7.0%
l 487
 
6.3%
d 464
 
6.0%
o 421
 
5.5%
a 400
 
5.2%
Other values (7) 1775
23.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7000
91.0%
Connector Punctuation 691
 
9.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 924
13.2%
s 768
11.0%
w 654
9.3%
t 567
8.1%
y 540
 
7.7%
l 487
 
7.0%
d 464
 
6.6%
o 421
 
6.0%
a 400
 
5.7%
h 400
 
5.7%
Other values (6) 1375
19.6%
Connector Punctuation
ValueCountFrequency (%)
_ 691
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7000
91.0%
Common 691
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 924
13.2%
s 768
11.0%
w 654
9.3%
t 567
8.1%
y 540
 
7.7%
l 487
 
7.0%
d 464
 
6.6%
o 421
 
6.0%
a 400
 
5.7%
h 400
 
5.7%
Other values (6) 1375
19.6%
Common
ValueCountFrequency (%)
_ 691
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 924
12.0%
s 768
10.0%
_ 691
 
9.0%
w 654
 
8.5%
t 567
 
7.4%
y 540
 
7.0%
l 487
 
6.3%
d 464
 
6.0%
o 421
 
5.5%
a 400
 
5.2%
Other values (7) 1775
23.1%

ethnicity
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
race_A
740 
race_B
123 
race_c
88 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters5706
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrace_A
2nd rowrace_B
3rd rowrace_A
4th rowrace_A
5th rowrace_A

Common Values

ValueCountFrequency (%)
race_A 740
77.8%
race_B 123
 
12.9%
race_c 88
 
9.3%

Length

2023-01-31T23:34:33.703673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T23:34:33.777740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
race_a 740
77.8%
race_b 123
 
12.9%
race_c 88
 
9.3%

Most occurring characters

ValueCountFrequency (%)
c 1039
18.2%
r 951
16.7%
a 951
16.7%
e 951
16.7%
_ 951
16.7%
A 740
13.0%
B 123
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3892
68.2%
Connector Punctuation 951
 
16.7%
Uppercase Letter 863
 
15.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 1039
26.7%
r 951
24.4%
a 951
24.4%
e 951
24.4%
Uppercase Letter
ValueCountFrequency (%)
A 740
85.7%
B 123
 
14.3%
Connector Punctuation
ValueCountFrequency (%)
_ 951
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4755
83.3%
Common 951
 
16.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 1039
21.9%
r 951
20.0%
a 951
20.0%
e 951
20.0%
A 740
15.6%
B 123
 
2.6%
Common
ValueCountFrequency (%)
_ 951
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5706
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 1039
18.2%
r 951
16.7%
a 951
16.7%
e 951
16.7%
_ 951
16.7%
A 740
13.0%
B 123
 
2.2%

ideology
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
conservative
353 
moderate
352 
liberal
203 
dont_know
43 

Length

Max length12
Median length9
Mean length9.3165089
Min length7

Characters and Unicode

Total characters8860
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmoderate
2nd rowliberal
3rd rowconservative
4th rowconservative
5th rowmoderate

Common Values

ValueCountFrequency (%)
conservative 353
37.1%
moderate 352
37.0%
liberal 203
21.3%
dont_know 43
 
4.5%

Length

2023-01-31T23:34:33.856812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T23:34:33.940888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
conservative 353
37.1%
moderate 352
37.0%
liberal 203
21.3%
dont_know 43
 
4.5%

Most occurring characters

ValueCountFrequency (%)
e 1613
18.2%
r 908
10.2%
a 908
10.2%
o 791
8.9%
t 748
8.4%
v 706
8.0%
i 556
 
6.3%
n 439
 
5.0%
l 406
 
4.6%
d 395
 
4.5%
Other values (7) 1390
15.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8817
99.5%
Connector Punctuation 43
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1613
18.3%
r 908
10.3%
a 908
10.3%
o 791
9.0%
t 748
8.5%
v 706
8.0%
i 556
 
6.3%
n 439
 
5.0%
l 406
 
4.6%
d 395
 
4.5%
Other values (6) 1347
15.3%
Connector Punctuation
ValueCountFrequency (%)
_ 43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8817
99.5%
Common 43
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1613
18.3%
r 908
10.3%
a 908
10.3%
o 791
9.0%
t 748
8.5%
v 706
8.0%
i 556
 
6.3%
n 439
 
5.0%
l 406
 
4.6%
d 395
 
4.5%
Other values (6) 1347
15.3%
Common
ValueCountFrequency (%)
_ 43
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8860
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1613
18.2%
r 908
10.2%
a 908
10.2%
o 791
8.9%
t 748
8.4%
v 706
8.0%
i 556
 
6.3%
n 439
 
5.0%
l 406
 
4.6%
d 395
 
4.5%
Other values (7) 1390
15.7%

kids
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
no
686 
yes
260 
dont_know
 
5

Length

Max length9
Median length2
Mean length2.3101998
Min length2

Characters and Unicode

Total characters2197
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowyes
3rd rowyes
4th rowyes
5th rowno

Common Values

ValueCountFrequency (%)
no 686
72.1%
yes 260
 
27.3%
dont_know 5
 
0.5%

Length

2023-01-31T23:34:34.042061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T23:34:34.139659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 686
72.1%
yes 260
 
27.3%
dont_know 5
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n 696
31.7%
o 696
31.7%
y 260
 
11.8%
e 260
 
11.8%
s 260
 
11.8%
d 5
 
0.2%
t 5
 
0.2%
_ 5
 
0.2%
k 5
 
0.2%
w 5
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2192
99.8%
Connector Punctuation 5
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 696
31.8%
o 696
31.8%
y 260
 
11.9%
e 260
 
11.9%
s 260
 
11.9%
d 5
 
0.2%
t 5
 
0.2%
k 5
 
0.2%
w 5
 
0.2%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2192
99.8%
Common 5
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 696
31.8%
o 696
31.8%
y 260
 
11.9%
e 260
 
11.9%
s 260
 
11.9%
d 5
 
0.2%
t 5
 
0.2%
k 5
 
0.2%
w 5
 
0.2%
Common
ValueCountFrequency (%)
_ 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2197
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 696
31.7%
o 696
31.7%
y 260
 
11.8%
e 260
 
11.8%
s 260
 
11.8%
d 5
 
0.2%
t 5
 
0.2%
_ 5
 
0.2%
k 5
 
0.2%
w 5
 
0.2%
Distinct24
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.550999
Minimum62
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-01-31T23:34:34.222755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile66
Q170
median74
Q379
95-th percentile83
Maximum85
Range23
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.18391
Coefficient of variation (CV)0.069535084
Kurtosis-0.95067349
Mean74.550999
Median Absolute Deviation (MAD)4
Skewness-0.099610677
Sum70898
Variance26.872923
MonotonicityNot monotonic
2023-01-31T23:34:34.304336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
69 66
 
6.9%
78 66
 
6.9%
73 62
 
6.5%
80 61
 
6.4%
77 60
 
6.3%
71 60
 
6.3%
72 57
 
6.0%
76 55
 
5.8%
79 53
 
5.6%
74 53
 
5.6%
Other values (14) 358
37.6%
ValueCountFrequency (%)
62 3
 
0.3%
63 6
 
0.6%
64 6
 
0.6%
65 18
 
1.9%
66 21
 
2.2%
67 35
3.7%
68 37
3.9%
69 66
6.9%
70 52
5.5%
71 60
6.3%
ValueCountFrequency (%)
85 1
 
0.1%
84 14
 
1.5%
83 35
3.7%
82 44
4.6%
81 48
5.0%
80 61
6.4%
79 53
5.6%
78 66
6.9%
77 60
6.3%
76 55
5.8%

pro_taxes_score
Real number (ℝ)

Distinct34
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.032597
Minimum21
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-01-31T23:34:34.393416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile28
Q134
median38
Q345
95-th percentile50
Maximum64
Range43
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.7551142
Coefficient of variation (CV)0.17306341
Kurtosis-0.49911374
Mean39.032597
Median Absolute Deviation (MAD)5
Skewness-0.010294723
Sum37120
Variance45.631568
MonotonicityNot monotonic
2023-01-31T23:34:34.486502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
35 74
 
7.8%
36 72
 
7.6%
33 60
 
6.3%
37 56
 
5.9%
34 48
 
5.0%
47 46
 
4.8%
46 44
 
4.6%
38 44
 
4.6%
48 42
 
4.4%
40 41
 
4.3%
Other values (24) 424
44.6%
ValueCountFrequency (%)
21 3
 
0.3%
22 2
 
0.2%
23 7
0.7%
24 7
0.7%
25 8
0.8%
26 5
 
0.5%
27 7
0.7%
28 11
1.2%
29 11
1.2%
30 16
1.7%
ValueCountFrequency (%)
64 1
 
0.1%
53 2
 
0.2%
52 5
 
0.5%
51 18
 
1.9%
50 29
3.0%
49 29
3.0%
48 42
4.4%
47 46
4.8%
46 44
4.6%
45 30
3.2%

pro_gunrights_score
Real number (ℝ)

Distinct58
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.010515
Minimum16
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-01-31T23:34:35.175128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile25
Q137
median45
Q354
95-th percentile66.5
Maximum75
Range59
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.282975
Coefficient of variation (CV)0.27289123
Kurtosis-0.61948069
Mean45.010515
Median Absolute Deviation (MAD)9
Skewness0.096804151
Sum42805
Variance150.87147
MonotonicityNot monotonic
2023-01-31T23:34:35.274218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 38
 
4.0%
44 35
 
3.7%
39 33
 
3.5%
45 33
 
3.5%
37 31
 
3.3%
40 31
 
3.3%
38 30
 
3.2%
49 28
 
2.9%
52 28
 
2.9%
47 28
 
2.9%
Other values (48) 636
66.9%
ValueCountFrequency (%)
16 1
 
0.1%
19 5
 
0.5%
20 6
 
0.6%
21 4
 
0.4%
22 6
 
0.6%
23 10
1.1%
24 9
0.9%
25 10
1.1%
26 18
1.9%
27 12
1.3%
ValueCountFrequency (%)
75 1
 
0.1%
74 2
 
0.2%
73 1
 
0.1%
72 2
 
0.2%
71 7
0.7%
70 10
1.1%
69 6
0.6%
68 9
0.9%
67 10
1.1%
66 8
0.8%

pro_healthcare_score
Real number (ℝ)

Distinct52
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.947424
Minimum18
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-01-31T23:34:35.378313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q128
median36
Q345
95-th percentile58
Maximum69
Range51
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.367607
Coefficient of variation (CV)0.30766982
Kurtosis-0.38669451
Mean36.947424
Median Absolute Deviation (MAD)9
Skewness0.49588746
Sum35137
Variance129.2225
MonotonicityNot monotonic
2023-01-31T23:34:35.484409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 44
 
4.6%
31 42
 
4.4%
26 41
 
4.3%
36 38
 
4.0%
27 38
 
4.0%
45 36
 
3.8%
43 36
 
3.8%
34 35
 
3.7%
33 32
 
3.4%
30 31
 
3.3%
Other values (42) 578
60.8%
ValueCountFrequency (%)
18 11
 
1.2%
19 30
3.2%
20 15
 
1.6%
21 9
 
0.9%
22 7
 
0.7%
23 29
3.0%
24 17
1.8%
25 28
2.9%
26 41
4.3%
27 38
4.0%
ValueCountFrequency (%)
69 1
 
0.1%
68 5
 
0.5%
67 2
 
0.2%
66 3
 
0.3%
65 2
 
0.2%
64 1
 
0.1%
63 1
 
0.1%
62 14
1.5%
61 7
0.7%
60 6
0.6%

pro_immigrants_score
Real number (ℝ)

Distinct58
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.478444
Minimum19
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-01-31T23:34:35.588504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile22
Q127
median34
Q344
95-th percentile59
Maximum80
Range61
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.889903
Coefficient of variation (CV)0.32594327
Kurtosis0.33071331
Mean36.478444
Median Absolute Deviation (MAD)8
Skewness0.83412105
Sum34691
Variance141.3698
MonotonicityNot monotonic
2023-01-31T23:34:35.686593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 47
 
4.9%
34 45
 
4.7%
36 42
 
4.4%
23 39
 
4.1%
26 38
 
4.0%
32 36
 
3.8%
25 36
 
3.8%
27 34
 
3.6%
33 33
 
3.5%
37 30
 
3.2%
Other values (48) 571
60.0%
ValueCountFrequency (%)
19 5
 
0.5%
20 17
 
1.8%
21 25
2.6%
22 30
3.2%
23 39
4.1%
24 47
4.9%
25 36
3.8%
26 38
4.0%
27 34
3.6%
28 21
2.2%
ValueCountFrequency (%)
80 1
 
0.1%
79 1
 
0.1%
77 1
 
0.1%
74 1
 
0.1%
73 5
0.5%
72 2
 
0.2%
71 1
 
0.1%
69 2
 
0.2%
68 1
 
0.1%
67 2
 
0.2%
Distinct45
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.166141
Minimum19
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-01-31T23:34:35.786720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile24
Q132
median39
Q347
95-th percentile54
Maximum73
Range54
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.4144206
Coefficient of variation (CV)0.24037141
Kurtosis-0.82318415
Mean39.166141
Median Absolute Deviation (MAD)8
Skewness0.11155475
Sum37247
Variance88.631316
MonotonicityNot monotonic
2023-01-31T23:34:35.888884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
39 46
 
4.8%
36 44
 
4.6%
40 44
 
4.6%
49 38
 
4.0%
27 37
 
3.9%
50 36
 
3.8%
38 34
 
3.6%
37 34
 
3.6%
35 33
 
3.5%
41 32
 
3.4%
Other values (35) 573
60.3%
ValueCountFrequency (%)
19 1
 
0.1%
20 1
 
0.1%
21 4
 
0.4%
22 2
 
0.2%
23 21
2.2%
24 24
2.5%
25 12
 
1.3%
26 30
3.2%
27 37
3.9%
28 25
2.6%
ValueCountFrequency (%)
73 1
 
0.1%
63 1
 
0.1%
62 1
 
0.1%
60 2
 
0.2%
59 2
 
0.2%
58 4
 
0.4%
57 7
 
0.7%
56 4
 
0.4%
55 8
 
0.8%
54 26
2.7%

environmentalist_score
Real number (ℝ)

Distinct12
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.176656
Minimum48
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-01-31T23:34:35.975999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile50
Q152
median53
Q355
95-th percentile57
Maximum59
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1423847
Coefficient of variation (CV)0.040288068
Kurtosis-0.49281891
Mean53.176656
Median Absolute Deviation (MAD)2
Skewness0.0090029608
Sum50571
Variance4.5898124
MonotonicityNot monotonic
2023-01-31T23:34:36.045061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
53 175
18.4%
54 145
15.2%
55 136
14.3%
52 136
14.3%
51 112
11.8%
56 86
9.0%
50 64
 
6.7%
49 42
 
4.4%
57 36
 
3.8%
58 12
 
1.3%
Other values (2) 7
 
0.7%
ValueCountFrequency (%)
48 3
 
0.3%
49 42
 
4.4%
50 64
 
6.7%
51 112
11.8%
52 136
14.3%
53 175
18.4%
54 145
15.2%
55 136
14.3%
56 86
9.0%
57 36
 
3.8%
ValueCountFrequency (%)
59 4
 
0.4%
58 12
 
1.3%
57 36
 
3.8%
56 86
9.0%
55 136
14.3%
54 145
15.2%
53 175
18.4%
52 136
14.3%
51 112
11.8%
50 64
 
6.7%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
46.0
946 
47.0
 
3
45.0
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3804
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46.0
2nd row46.0
3rd row46.0
4th row46.0
5th row46.0

Common Values

ValueCountFrequency (%)
46.0 946
99.5%
47.0 3
 
0.3%
45.0 2
 
0.2%

Length

2023-01-31T23:34:36.121131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T23:34:36.194197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
46.0 946
99.5%
47.0 3
 
0.3%
45.0 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
4 951
25.0%
. 951
25.0%
0 951
25.0%
6 946
24.9%
7 3
 
0.1%
5 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2853
75.0%
Other Punctuation 951
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 951
33.3%
0 951
33.3%
6 946
33.2%
7 3
 
0.1%
5 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 951
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3804
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 951
25.0%
. 951
25.0%
0 951
25.0%
6 946
24.9%
7 3
 
0.1%
5 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 951
25.0%
. 951
25.0%
0 951
25.0%
6 946
24.9%
7 3
 
0.1%
5 2
 
0.1%

economic_populist_score
Real number (ℝ)

Distinct48
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.039958
Minimum13
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-01-31T23:34:36.272268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile18
Q125
median33
Q343
95-th percentile54
Maximum60
Range47
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.20303
Coefficient of variation (CV)0.32911409
Kurtosis-0.85824461
Mean34.039958
Median Absolute Deviation (MAD)9
Skewness0.3319833
Sum32372
Variance125.50788
MonotonicityNot monotonic
2023-01-31T23:34:36.369906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
23 43
 
4.5%
22 41
 
4.3%
29 37
 
3.9%
27 37
 
3.9%
28 35
 
3.7%
26 32
 
3.4%
38 31
 
3.3%
25 30
 
3.2%
24 29
 
3.0%
37 29
 
3.0%
Other values (38) 607
63.8%
ValueCountFrequency (%)
13 2
 
0.2%
14 4
 
0.4%
15 7
 
0.7%
16 12
 
1.3%
17 14
 
1.5%
18 17
1.8%
19 16
 
1.7%
20 23
2.4%
21 23
2.4%
22 41
4.3%
ValueCountFrequency (%)
60 4
 
0.4%
59 6
 
0.6%
58 8
 
0.8%
57 6
 
0.6%
56 5
 
0.5%
55 10
1.1%
54 10
1.1%
53 13
1.4%
52 9
 
0.9%
51 24
2.5%

pro_military_score
Real number (ℝ)

Distinct39
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.930599
Minimum37
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-01-31T23:34:36.464993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile41
Q150
median56
Q363
95-th percentile69
Maximum75
Range38
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.3903089
Coefficient of variation (CV)0.15001286
Kurtosis-0.77902276
Mean55.930599
Median Absolute Deviation (MAD)7
Skewness-0.06643843
Sum53190
Variance70.397284
MonotonicityNot monotonic
2023-01-31T23:34:36.556075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
65 46
 
4.8%
64 46
 
4.8%
57 44
 
4.6%
54 43
 
4.5%
52 43
 
4.5%
51 42
 
4.4%
55 40
 
4.2%
58 36
 
3.8%
62 35
 
3.7%
46 35
 
3.7%
Other values (29) 541
56.9%
ValueCountFrequency (%)
37 2
 
0.2%
38 6
 
0.6%
39 7
 
0.7%
40 17
1.8%
41 18
1.9%
42 16
1.7%
43 14
 
1.5%
44 10
 
1.1%
45 14
 
1.5%
46 35
3.7%
ValueCountFrequency (%)
75 2
 
0.2%
74 4
 
0.4%
73 2
 
0.2%
72 8
 
0.8%
71 14
1.5%
70 13
1.4%
69 17
1.8%
68 16
1.7%
67 29
3.0%
66 25
2.6%

pro_regulation_score
Real number (ℝ)

Distinct29
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.985279
Minimum41
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-01-31T23:34:36.648159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile48
Q154
median59
Q362
95-th percentile66
Maximum69
Range28
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.4112742
Coefficient of variation (CV)0.093321517
Kurtosis-0.29586035
Mean57.985279
Median Absolute Deviation (MAD)4
Skewness-0.54776467
Sum55144
Variance29.281888
MonotonicityNot monotonic
2023-01-31T23:34:36.728951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
61 80
 
8.4%
60 78
 
8.2%
62 70
 
7.4%
63 67
 
7.0%
59 66
 
6.9%
58 58
 
6.1%
64 58
 
6.1%
57 54
 
5.7%
55 50
 
5.3%
56 47
 
4.9%
Other values (19) 323
34.0%
ValueCountFrequency (%)
41 1
 
0.1%
42 2
 
0.2%
43 2
 
0.2%
44 5
 
0.5%
45 6
 
0.6%
46 11
 
1.2%
47 13
1.4%
48 18
1.9%
49 23
2.4%
50 28
2.9%
ValueCountFrequency (%)
69 3
 
0.3%
68 6
 
0.6%
67 10
 
1.1%
66 30
 
3.2%
65 35
3.7%
64 58
6.1%
63 67
7.0%
62 70
7.4%
61 80
8.4%
60 78
8.2%

traditionalist_score
Real number (ℝ)

Distinct37
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.762355
Minimum31
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-01-31T23:34:36.818313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile38
Q145
median50
Q355
95-th percentile61
Maximum67
Range36
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.8899845
Coefficient of variation (CV)0.13845777
Kurtosis-0.51661782
Mean49.762355
Median Absolute Deviation (MAD)5
Skewness-0.14487276
Sum47324
Variance47.471887
MonotonicityNot monotonic
2023-01-31T23:34:36.910426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
48 69
 
7.3%
47 52
 
5.5%
50 52
 
5.5%
53 51
 
5.4%
52 45
 
4.7%
46 44
 
4.6%
45 44
 
4.6%
51 44
 
4.6%
44 43
 
4.5%
49 42
 
4.4%
Other values (27) 465
48.9%
ValueCountFrequency (%)
31 1
 
0.1%
32 1
 
0.1%
33 2
 
0.2%
34 7
 
0.7%
35 6
 
0.6%
36 14
1.5%
37 14
1.5%
38 18
1.9%
39 20
2.1%
40 14
1.5%
ValueCountFrequency (%)
67 2
 
0.2%
66 2
 
0.2%
65 2
 
0.2%
64 2
 
0.2%
63 5
 
0.5%
62 14
 
1.5%
61 28
2.9%
60 20
2.1%
59 26
2.7%
58 40
4.2%

compassionate_score
Real number (ℝ)

Distinct41
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.332282
Minimum31
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-01-31T23:34:37.011518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile39
Q145
median53
Q362
95-th percentile68
Maximum71
Range40
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.7075662
Coefficient of variation (CV)0.18202045
Kurtosis-1.1901069
Mean53.332282
Median Absolute Deviation (MAD)9
Skewness-0.035864811
Sum50719
Variance94.236841
MonotonicityNot monotonic
2023-01-31T23:34:37.107640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
65 41
 
4.3%
50 40
 
4.2%
66 39
 
4.1%
43 37
 
3.9%
67 37
 
3.9%
41 35
 
3.7%
68 34
 
3.6%
58 34
 
3.6%
42 34
 
3.6%
52 32
 
3.4%
Other values (31) 588
61.8%
ValueCountFrequency (%)
31 1
 
0.1%
32 3
 
0.3%
33 4
 
0.4%
34 1
 
0.1%
35 5
 
0.5%
36 3
 
0.3%
37 9
 
0.9%
38 14
1.5%
39 25
2.6%
40 26
2.7%
ValueCountFrequency (%)
71 2
 
0.2%
70 8
 
0.8%
69 12
 
1.3%
68 34
3.6%
67 37
3.9%
66 39
4.1%
65 41
4.3%
64 29
3.0%
63 20
2.1%
62 30
3.2%

pro_free_trade_score
Real number (ℝ)

Distinct37
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.42061
Minimum19
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-01-31T23:34:37.204728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile30
Q134
median39
Q344
95-th percentile51
Maximum65
Range46
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.8388793
Coefficient of variation (CV)0.17348487
Kurtosis0.083581383
Mean39.42061
Median Absolute Deviation (MAD)5
Skewness0.59798599
Sum37489
Variance46.77027
MonotonicityNot monotonic
2023-01-31T23:34:37.295812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
33 74
 
7.8%
36 64
 
6.7%
39 54
 
5.7%
34 54
 
5.7%
38 47
 
4.9%
35 47
 
4.9%
31 46
 
4.8%
42 45
 
4.7%
41 43
 
4.5%
43 41
 
4.3%
Other values (27) 436
45.8%
ValueCountFrequency (%)
19 1
 
0.1%
27 2
 
0.2%
28 14
 
1.5%
29 12
 
1.3%
30 28
 
2.9%
31 46
4.8%
32 40
4.2%
33 74
7.8%
34 54
5.7%
35 47
4.9%
ValueCountFrequency (%)
65 1
 
0.1%
62 1
 
0.1%
60 4
0.4%
59 4
0.4%
58 4
0.4%
57 2
 
0.2%
56 6
0.6%
55 5
0.5%
54 7
0.7%
53 6
0.6%

pro_globalism_score
Real number (ℝ)

Distinct39
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.487907
Minimum27
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-01-31T23:34:37.394901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile35
Q138
median42
Q348
95-th percentile58
Maximum67
Range40
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.1677355
Coefficient of variation (CV)0.16482135
Kurtosis0.27421645
Mean43.487907
Median Absolute Deviation (MAD)5
Skewness0.83126085
Sum41357
Variance51.376433
MonotonicityNot monotonic
2023-01-31T23:34:37.486985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
37 80
 
8.4%
39 77
 
8.1%
40 68
 
7.2%
38 64
 
6.7%
36 55
 
5.8%
41 50
 
5.3%
47 45
 
4.7%
49 44
 
4.6%
44 42
 
4.4%
43 42
 
4.4%
Other values (29) 384
40.4%
ValueCountFrequency (%)
27 1
 
0.1%
30 2
 
0.2%
31 5
 
0.5%
32 6
 
0.6%
33 10
 
1.1%
34 16
 
1.7%
35 33
3.5%
36 55
5.8%
37 80
8.4%
38 64
6.7%
ValueCountFrequency (%)
67 2
 
0.2%
66 2
 
0.2%
65 2
 
0.2%
64 7
0.7%
63 4
 
0.4%
62 2
 
0.2%
61 3
 
0.3%
60 10
1.1%
59 10
1.1%
58 10
1.1%
Distinct48
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.607781
Minimum26
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-01-31T23:34:37.588078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile33
Q142
median49
Q358
95-th percentile67
Maximum73
Range47
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.262897
Coefficient of variation (CV)0.20688079
Kurtosis-0.81875331
Mean49.607781
Median Absolute Deviation (MAD)8
Skewness0.032901021
Sum47177
Variance105.32706
MonotonicityNot monotonic
2023-01-31T23:34:37.688169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
44 44
 
4.6%
47 38
 
4.0%
43 37
 
3.9%
58 35
 
3.7%
49 34
 
3.6%
35 33
 
3.5%
54 32
 
3.4%
46 32
 
3.4%
53 32
 
3.4%
56 31
 
3.3%
Other values (38) 603
63.4%
ValueCountFrequency (%)
26 1
 
0.1%
27 1
 
0.1%
28 1
 
0.1%
29 7
 
0.7%
30 5
 
0.5%
31 13
 
1.4%
32 15
1.6%
33 7
 
0.7%
34 10
 
1.1%
35 33
3.5%
ValueCountFrequency (%)
73 2
 
0.2%
72 4
 
0.4%
71 7
 
0.7%
70 5
 
0.5%
69 5
 
0.5%
68 9
0.9%
67 18
1.9%
66 8
0.8%
65 16
1.7%
64 18
1.9%

pro_populism_score
Real number (ℝ)

Distinct38
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.004206
Minimum19
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-01-31T23:34:37.783255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile23
Q127
median31
Q336
95-th percentile47
Maximum56
Range37
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.0106673
Coefficient of variation (CV)0.21905456
Kurtosis0.47867248
Mean32.004206
Median Absolute Deviation (MAD)4
Skewness0.86749656
Sum30436
Variance49.149456
MonotonicityNot monotonic
2023-01-31T23:34:37.875339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
29 68
 
7.2%
31 64
 
6.7%
30 63
 
6.6%
28 62
 
6.5%
26 61
 
6.4%
27 59
 
6.2%
32 52
 
5.5%
33 46
 
4.8%
36 45
 
4.7%
34 42
 
4.4%
Other values (28) 389
40.9%
ValueCountFrequency (%)
19 1
 
0.1%
20 5
 
0.5%
21 14
 
1.5%
22 20
 
2.1%
23 37
3.9%
24 34
3.6%
25 40
4.2%
26 61
6.4%
27 59
6.2%
28 62
6.5%
ValueCountFrequency (%)
56 1
 
0.1%
55 3
 
0.3%
54 1
 
0.1%
53 4
 
0.4%
52 2
 
0.2%
51 4
 
0.4%
50 4
 
0.4%
49 12
1.3%
48 6
0.6%
47 12
1.3%
Distinct68
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.480547
Minimum6
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-01-31T23:34:37.982436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile13
Q121
median34
Q369
95-th percentile77
Maximum80
Range74
Interquartile range (IQR)48

Descriptive statistics

Standard deviation24.299261
Coefficient of variation (CV)0.55885363
Kurtosis-1.7205913
Mean43.480547
Median Absolute Deviation (MAD)20
Skewness0.14630859
Sum41350
Variance590.45409
MonotonicityNot monotonic
2023-01-31T23:34:38.082527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 64
 
6.7%
20 53
 
5.6%
21 48
 
5.0%
74 45
 
4.7%
19 41
 
4.3%
73 37
 
3.9%
25 35
 
3.7%
72 30
 
3.2%
24 30
 
3.2%
71 29
 
3.0%
Other values (58) 539
56.7%
ValueCountFrequency (%)
6 1
 
0.1%
8 4
 
0.4%
9 4
 
0.4%
10 10
 
1.1%
11 11
1.2%
12 17
1.8%
13 12
1.3%
14 7
 
0.7%
15 27
2.8%
16 19
2.0%
ValueCountFrequency (%)
80 1
 
0.1%
79 11
 
1.2%
78 22
2.3%
77 19
2.0%
76 8
 
0.8%
75 11
 
1.2%
74 45
4.7%
73 37
3.9%
72 30
3.2%
71 29
3.0%

racial_resentment_score
Real number (ℝ)

Distinct45
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.08307
Minimum35
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-01-31T23:34:38.183619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile52
Q163
median70
Q377
95-th percentile82
Maximum84
Range49
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.5155517
Coefficient of variation (CV)0.13774072
Kurtosis-0.24127735
Mean69.08307
Median Absolute Deviation (MAD)7
Skewness-0.5980208
Sum65698
Variance90.545724
MonotonicityNot monotonic
2023-01-31T23:34:38.280707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
81 48
 
5.0%
79 47
 
4.9%
77 47
 
4.9%
78 47
 
4.9%
66 44
 
4.6%
76 42
 
4.4%
71 42
 
4.4%
68 38
 
4.0%
73 36
 
3.8%
67 32
 
3.4%
Other values (35) 528
55.5%
ValueCountFrequency (%)
35 2
 
0.2%
40 2
 
0.2%
41 1
 
0.1%
42 1
 
0.1%
43 3
0.3%
44 2
 
0.2%
46 3
0.3%
47 2
 
0.2%
48 4
0.4%
49 5
0.5%
ValueCountFrequency (%)
84 5
 
0.5%
83 15
 
1.6%
82 30
3.2%
81 48
5.0%
80 32
3.4%
79 47
4.9%
78 47
4.9%
77 47
4.9%
76 42
4.4%
75 31
3.3%
Distinct37
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.008412
Minimum34
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.9 KiB
2023-01-31T23:34:38.375794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile38
Q145
median51
Q355
95-th percentile62
Maximum70
Range36
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.2450297
Coefficient of variation (CV)0.14487622
Kurtosis-0.43357814
Mean50.008412
Median Absolute Deviation (MAD)5
Skewness0.056395019
Sum47558
Variance52.490455
MonotonicityNot monotonic
2023-01-31T23:34:38.466877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
51 59
 
6.2%
55 52
 
5.5%
50 52
 
5.5%
53 52
 
5.5%
54 49
 
5.2%
52 49
 
5.2%
56 46
 
4.8%
45 44
 
4.6%
44 44
 
4.6%
57 41
 
4.3%
Other values (27) 463
48.7%
ValueCountFrequency (%)
34 2
 
0.2%
35 8
 
0.8%
36 13
 
1.4%
37 16
1.7%
38 17
1.8%
39 20
2.1%
40 38
4.0%
41 24
2.5%
42 29
3.0%
43 25
2.6%
ValueCountFrequency (%)
70 2
 
0.2%
69 3
 
0.3%
68 4
 
0.4%
67 5
 
0.5%
66 6
0.6%
65 5
 
0.5%
64 9
0.9%
63 8
0.8%
62 8
0.8%
61 13
1.4%

Interactions

2023-01-31T23:34:30.238702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:54.275041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:56.253564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:58.056921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:00.052807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:01.969673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:03.993513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:06.159992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:08.096312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:10.280809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:12.350297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:14.276557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:16.227332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:18.647057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:20.432207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:22.353953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:24.198156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:26.549804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:28.461068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:30.332788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:54.387653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:56.347650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:58.149006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:00.144399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:02.090784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:04.349838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:06.254586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:08.192400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:10.386904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:12.447386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:14.375648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:16.327422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-01-31T23:33:59.203473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:01.197069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:03.269856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:05.454842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:07.375113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:09.271891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:11.590507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:13.557395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:15.504674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:17.911389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:19.755064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:21.626292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:23.509005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:25.857666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:27.751406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:29.581596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:31.528928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:55.546887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:57.481398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:59.296066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:01.288001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:03.373950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:05.557937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:07.476205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:09.367978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:11.695602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:13.653482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:15.604765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:18.012482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:19.845147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:21.721380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:23.607603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:25.952005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:27.847492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:29.670677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:31.633533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:55.645977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:57.582490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:59.398670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:01.399102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:03.485051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:05.665033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:07.589309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:09.473073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:11.808754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:13.757577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:15.721872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:18.124582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:19.944237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:21.831478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:23.712698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:26.054353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:27.953099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:29.766763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:31.727618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:55.737078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:57.671571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:59.662994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:01.512714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:03.589146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:05.759119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:07.684395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:09.567158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:11.913850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:13.853664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:15.817959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:18.225674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:20.036320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:21.928567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:23.810295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:26.149440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:28.049186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:29.855845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:31.829710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:55.833165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:57.767658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:59.759015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:01.641831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:03.692239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:05.858209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:07.787489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:09.666249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:12.021456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:13.962764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:15.920052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:18.336775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:20.132408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:22.041670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:23.909386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:26.249531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:28.153280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:29.951440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:31.932823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:56.064392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:57.871753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:59.860106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:01.753477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:03.797334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:05.961303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:07.890091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:10.077623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:12.130589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:14.065860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:16.027150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:18.447876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:20.235519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:22.158776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:24.006474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:26.353625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:28.259886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:30.047528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:32.027910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:56.155475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:57.960834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:33:59.948695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:01.855569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:03.891420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:06.053386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:07.984211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:10.171709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:12.235193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:14.163455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:16.123237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:18.544965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:20.330605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:22.251860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:24.096555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:26.445709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:28.358976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-31T23:34:30.136609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-01-31T23:34:38.580488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
pro_authoritarianism_scorepro_taxes_scorepro_gunrights_scorepro_healthcare_scorepro_immigrants_scorepro_supporting_the_poor_scoreenvironmentalist_scoreeconomic_populist_scorepro_military_scorepro_regulation_scoretraditionalist_scorecompassionate_scorepro_free_trade_scorepro_globalism_scorepro_healthcare_women_scorepro_populism_scorepresidential_election_turnout_scoreracial_resentment_scorepro_religious_freedom_scoresupport_initiativeregioncountyeducationsesethnicityideologykidstrust_in_institutions_score
pro_authoritarianism_score1.000-0.5850.212-0.590-0.630-0.5970.766-0.6420.8640.1360.827-0.755-0.126-0.296-0.6850.1580.6130.7610.6380.2380.1000.1390.0690.0390.0870.2910.0520.000
pro_taxes_score-0.5851.000-0.5330.4890.1420.806-0.6900.489-0.6650.434-0.6240.867-0.502-0.2000.633-0.613-0.394-0.402-0.8490.2170.1270.1030.0440.0290.2780.2450.0320.498
pro_gunrights_score0.212-0.5331.000-0.321-0.251-0.4350.334-0.3110.280-0.6140.151-0.5230.4650.122-0.3620.4880.4300.3220.4570.1440.1140.0780.1170.0000.0390.1510.1520.050
pro_healthcare_score-0.5900.489-0.3211.0000.7960.604-0.8330.880-0.807-0.285-0.7210.7020.3120.6490.9030.195-0.727-0.880-0.6420.1930.1430.1630.0720.0890.0960.2050.1130.101
pro_immigrants_score-0.6300.142-0.2510.7961.0000.285-0.6310.739-0.667-0.422-0.5790.4370.6040.7940.6710.376-0.751-0.899-0.3760.1820.1730.1640.0680.0410.3140.2210.1380.054
pro_supporting_the_poor_score-0.5970.806-0.4350.6040.2851.000-0.5960.729-0.6450.270-0.5000.906-0.3140.1520.711-0.234-0.538-0.588-0.5770.2000.1070.1270.0000.0750.0650.2260.0000.497
environmentalist_score0.766-0.6900.334-0.833-0.631-0.5961.000-0.7260.9220.1230.905-0.789-0.123-0.328-0.9000.1850.6060.7470.8540.1950.1240.1260.0740.0530.0250.2560.1210.130
economic_populist_score-0.6420.489-0.3110.8800.7390.729-0.7261.000-0.752-0.229-0.5790.7420.2700.6630.8710.231-0.787-0.879-0.4870.2110.1230.1800.0000.0660.1690.2550.1310.133
pro_military_score0.864-0.6650.280-0.807-0.667-0.6450.922-0.7521.0000.1690.920-0.830-0.165-0.371-0.8810.1710.6650.8130.7800.2120.2050.1980.0750.0490.0980.2730.0720.000
pro_regulation_score0.1360.434-0.614-0.285-0.4220.2700.123-0.2290.1691.0000.2280.208-0.800-0.658-0.148-0.7140.0910.296-0.1380.1120.0560.0610.0650.0860.1840.1290.1810.030
traditionalist_score0.827-0.6240.151-0.721-0.579-0.5000.905-0.5790.9200.2281.000-0.732-0.174-0.242-0.7760.2040.5110.6920.8240.1850.1440.1240.0640.0510.1470.2490.0700.000
compassionate_score-0.7550.867-0.5230.7020.4370.906-0.7890.742-0.8300.208-0.7321.000-0.2540.1450.801-0.344-0.608-0.716-0.7680.2440.1200.1420.0400.0830.1450.2730.0770.000
pro_free_trade_score-0.126-0.5020.4650.3120.604-0.314-0.1230.270-0.165-0.800-0.174-0.2541.0000.7610.1900.723-0.246-0.3860.1860.0000.1340.0790.0000.0270.2840.0940.2280.494
pro_globalism_score-0.296-0.2000.1220.6490.7940.152-0.3280.663-0.371-0.658-0.2420.1450.7611.0000.4800.750-0.534-0.7170.0100.0660.1470.1150.0090.0000.3400.0820.1590.226
pro_healthcare_women_score-0.6850.633-0.3620.9030.6710.711-0.9000.871-0.881-0.148-0.7760.8010.1900.4801.000-0.034-0.700-0.820-0.7210.2060.1730.1700.0470.0960.0390.2520.1080.055
pro_populism_score0.158-0.6130.4880.1950.376-0.2340.1850.2310.171-0.7140.204-0.3440.7230.750-0.0341.000-0.106-0.2130.5110.0850.0980.0310.1280.0000.3640.1200.1190.121
presidential_election_turnout_score0.613-0.3940.430-0.727-0.751-0.5380.606-0.7870.6650.0910.511-0.608-0.246-0.534-0.700-0.1061.0000.7930.4450.2530.2840.2040.0000.0590.2670.2540.0150.038
racial_resentment_score0.761-0.4020.322-0.880-0.899-0.5880.747-0.8790.8130.2960.692-0.716-0.386-0.717-0.820-0.2130.7931.0000.5280.2290.1730.1790.0520.0730.1910.2660.0500.154
pro_religious_freedom_score0.638-0.8490.457-0.642-0.376-0.5770.854-0.4870.780-0.1380.824-0.7680.1860.010-0.7210.5110.4450.5281.0000.1880.1210.0900.0640.0630.2370.2570.0000.000
support_initiative0.2380.2170.1440.1930.1820.2000.1950.2110.2120.1120.1850.2440.0000.0660.2060.0850.2530.2290.1881.0000.0450.0530.0920.1030.0190.2880.0250.025
region0.1000.1270.1140.1430.1730.1070.1240.1230.2050.0560.1440.1200.1340.1470.1730.0980.2840.1730.1210.0451.0000.6890.0540.0520.0660.0610.0290.015
county0.1390.1030.0780.1630.1640.1270.1260.1800.1980.0610.1240.1420.0790.1150.1700.0310.2040.1790.0900.0530.6891.0000.0590.0300.0000.0740.0220.000
education0.0690.0440.1170.0720.0680.0000.0740.0000.0750.0650.0640.0400.0000.0090.0470.1280.0000.0520.0640.0920.0540.0591.0000.1360.0400.0870.0980.000
ses0.0390.0290.0000.0890.0410.0750.0530.0660.0490.0860.0510.0830.0270.0000.0960.0000.0590.0730.0630.1030.0520.0300.1361.0000.0620.0770.1540.068
ethnicity0.0870.2780.0390.0960.3140.0650.0250.1690.0980.1840.1470.1450.2840.3400.0390.3640.2670.1910.2370.0190.0660.0000.0400.0621.0000.0570.0440.000
ideology0.2910.2450.1510.2050.2210.2260.2560.2550.2730.1290.2490.2730.0940.0820.2520.1200.2540.2660.2570.2880.0610.0740.0870.0770.0571.0000.1410.036
kids0.0520.0320.1520.1130.1380.0000.1210.1310.0720.1810.0700.0770.2280.1590.1080.1190.0150.0500.0000.0250.0290.0220.0980.1540.0440.1411.0000.000
trust_in_institutions_score0.0000.4980.0500.1010.0540.4970.1300.1330.0000.0300.0000.0000.4940.2260.0550.1210.0380.1540.0000.0250.0150.0000.0000.0680.0000.0360.0001.000

Missing values

2023-01-31T23:34:32.202577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-31T23:34:32.678041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

support_initiativeregioncountyeducationsesethnicityideologykidspro_authoritarianism_scorepro_taxes_scorepro_gunrights_scorepro_healthcare_scorepro_immigrants_scorepro_supporting_the_poor_scoreenvironmentalist_scoretrust_in_institutions_scoreeconomic_populist_scorepro_military_scorepro_regulation_scoretraditionalist_scorecompassionate_scorepro_free_trade_scorepro_globalism_scorepro_healthcare_women_scorepro_populism_scorepresidential_election_turnout_scoreracial_resentment_scorepro_religious_freedom_score
0yeswestduchesscollege_graduatemid_sesrace_Amoderateyes69.050.039.062.050.051.049.046.054.040.054.038.066.042.049.071.031.015.055.036.0
2yeseastllandilocollege_graduatewealthyrace_Bliberalyes73.034.044.035.050.038.054.046.040.056.060.052.050.048.049.051.037.013.067.057.0
3noeastcuyacollege_graduatewealthyrace_Aconservativeyes77.034.060.028.032.026.053.046.023.057.056.049.043.046.040.041.030.071.077.052.0
4noeastcuyacollege_graduatevery_wealthyrace_Aconservativeyes76.034.069.032.026.035.053.046.029.059.051.047.050.040.042.043.037.076.075.055.0
5yeswestcuyasome_collegewealthyrace_Amoderateno76.042.042.031.026.037.053.046.031.060.062.055.052.030.038.044.030.025.078.050.0
6noeastllandilopost_graduatedont_knowrace_Amoderateno81.039.042.033.024.040.055.046.027.061.062.055.050.033.039.044.029.072.077.052.0
7yeseastcuyapost_graduatemid_sesrace_Amoderateno75.036.068.040.037.034.053.046.031.052.049.046.047.048.047.053.036.031.069.050.0
8yeswestcuyapost_graduatewealthyrace_Aliberalno70.042.046.023.029.033.053.046.021.055.063.050.052.033.034.042.025.031.078.050.0
9nowestcuyasome_collegewealthyrace_Aconservativeno85.036.058.027.020.039.057.046.032.072.058.062.043.037.039.041.037.072.083.059.0
10yeswestcuyacollege_graduatewealthyrace_Amoderateno83.024.042.031.040.027.056.046.029.071.058.061.037.047.050.040.049.069.076.068.0
support_initiativeregioncountyeducationsesethnicityideologykidspro_authoritarianism_scorepro_taxes_scorepro_gunrights_scorepro_healthcare_scorepro_immigrants_scorepro_supporting_the_poor_scoreenvironmentalist_scoretrust_in_institutions_scoreeconomic_populist_scorepro_military_scorepro_regulation_scoretraditionalist_scorecompassionate_scorepro_free_trade_scorepro_globalism_scorepro_healthcare_women_scorepro_populism_scorepresidential_election_turnout_scoreracial_resentment_scorepro_religious_freedom_score
1088nosouthcuyahigh_schoolwealthyrace_Aconservativeno70.044.053.054.040.044.050.046.047.045.048.040.062.037.045.062.038.067.066.045.0
1089yessouthcuyapost_graduatemid_sesrace_Aliberalno78.036.061.023.021.037.055.046.022.065.060.057.046.034.040.037.032.078.079.056.0
1090yeswestcuyasome_collegemid_sesrace_cconservativeno73.050.034.043.030.055.052.046.043.055.064.050.065.028.041.056.027.026.068.042.0
1091nowestduchesspost_graduatewealthyrace_cmoderateno70.042.039.031.037.038.052.046.034.052.061.045.057.041.038.058.025.021.069.047.0
1092yesnorthangelocollege_graduatedont_knowrace_Amoderateno72.031.060.037.043.028.053.046.029.052.047.046.048.052.051.056.036.071.060.053.0
1093nowestcuyasome_collegemid_sesrace_Aconservativeno80.035.039.031.028.035.055.046.027.063.057.056.047.036.043.045.030.068.075.053.0
1094yeseastcuyahigh_schoolmid_sesrace_Adont_knowyes78.031.072.034.035.025.054.046.029.059.048.051.038.051.048.043.040.031.073.055.0
1096nonorthangelocollege_graduatewealthyrace_Amoderateyes81.033.064.023.022.030.056.046.023.065.059.056.040.042.037.040.032.068.083.059.0
1097yessouthcuyasome_collegemid_sesrace_Aconservativeno81.032.062.018.023.031.058.046.021.073.057.062.039.039.039.028.036.032.081.061.0
1098yeseastcuyacollege_graduatevery_wealthyrace_Amoderateno63.049.047.053.051.047.049.046.043.037.055.035.065.045.046.068.027.019.056.037.0